Semantic Segmentation. The labels provided by the Taskonomy dataset on semantic segmentation are generated through a network pre-trained on the MSCOCO [1]} dataset. Our selected subset contains 17 semantic classes. We apply random flip, color jitter, and normalization for data augmentation. For this task, we use the SGD optimizer with a learning rate 0.1, along with a cosine annealing scheduler. Similar to the settings in Taskonomy, each network is trained for 30 epochs. The decoder, optimizer, and learning rate scheduler settings are the same as above.
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